Description Usage Arguments Value References See Also Examples

This function computes the Multiple Correspondence Analysis (MCA) solution on the indicator matrix using two incremental methods described in Iodice D'Enza & Markos (2015)

1 2 |

`data1` |
Matrix or data frame of starting data or full data if data2 = NULL |

`data2` |
Matrix or data frame of incoming data |

`method` |
String specifying the type of implementation: |

`current_rank` |
Rank of approximation or number of components to compute; if empty, the full rank is used |

`nchunk` |
Number of incoming data chunks (equal splits of 'data2', |

`ff` |
Number between 0 and 1 indicating the "forgetting factor" used to down-weight the contribution of earlier data blocks to the current solution. When |

`disk` |
Logical indicating whether then output is saved to hard disk |

`rowpcoord` |
Row principal coordinates |

`colpcoord` |
Column principal coordinates |

`rowcoord` |
Row standard coordinates |

`colcoord` |
Column standard coordinates |

`sv` |
Singular values |

`inertia.e` |
Percentages of explained inertia |

`levelnames` |
Column labels |

`rowctr` |
Row contributions |

`colctr` |
Column contributions |

`rowcor` |
Row squared correlations |

`colcor` |
Column squared correlations |

`rowmass` |
Row masses |

`colmass` |
Column masses |

`nchunk` |
A copy of |

`disk` |
A copy of |

`ff` |
A copy of |

`allrowcoord` |
A list containing the row principal coordinates produced after each data chunk is analyzed; returned only when |

`allcolcoord` |
A list containing the column principal coordinates on the principal components produced after each data chunk is analyzed; returned only when |

`allrowctr` |
A list containing the row contributions after each data chunk is analyzed; returned only when |

`allcolctr` |
A list containing the column contributions after each data chunk is analyzed; returned only when |

`allrowcor` |
A list containing the row squared correlations produced after each data chunk is analyzed; returned only when |

`allcolcor` |
A list containing the column squared correlations produced after each data chunk is analyzed; returned only when |

Hall, P., Marshall, D., & Martin, R. (2002). Adding and subtracting eigenspaces with eigenvalue decomposition and singular value decomposition. *Image and Vision Computing*, *20*(13), 1009-1016.

Ross, D. A., Lim, J., Lin, R. S., & Yang, M. H. (2008). Incremental learning for robust visual tracking. *International Journal of Computer Vision*, *77*(1-3), 125-141.

Iodice D' Enza, A., & Markos, A. (2015). Low-dimensional tracking of association structures in categorical data, *Statistics and Computing*, *25*(5), 1009-1022.

`update.i_mca`

, `i_pca`

, `update.i_pca`

, `add_es`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | ```
##Example 1 - Exact case
data("women", package = "idm")
nc = 5 # number of chunks
res_iMCAh = i_mca(data1 = women[1:300,1:7], data2 = women[301:2107,1:7]
,method = "exact", nchunk = nc)
#static MCA plot of attributes on axes 2 and 3
plot(x = res_iMCAh, dim = c(2,3), what = c(FALSE,TRUE), animation = FALSE)
#\donttest is used here because the code calls the saveLatex function of the animation package
#which requires ImageMagick or GraphicsMagick and
#Adobe Acrobat Reader to be installed in your system
#Creates animated plot in PDF for objects and variables
plot(res_iMCAh, animation = TRUE, frames = 10, movie_format = 'pdf')
##Example 2 - Live case
data("tweet", package = "idm")
nc = 5
#provide attributes with custom labels
labels = c("HLTN", "ICN", "MRT","BWN","SWD","HYT","CH", "-", "-/+", "+", "++", "Low", "Med","High")
#mimics the 'live' MCA implementation
res_iMCAl = i_mca(data1 = tweet[1:100,], data2 = tweet[101:1000,],
method="live", nchunk = nc, current_rank = 2)
#\donttest is used here because the code calls the saveLatex function of the animation package
#which requires ImageMagick or GraphicsMagick and
#Adobe Acrobat Reader to be installed in your system
#See help(im.convert) for details on the configuration of ImageMagick or GraphicsMagick.
#Creates animated plot in PDF for observations and variables
plot(res_iMCAl, labels = labels, animation = TRUE, frames = 10, movie_format = 'pdf')
``` |

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